

import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def   __UpperCAmelCase					(      __a :     bytes						,__a :     int      )     ->	np.array:

					"""simple docstring"""

					_a      :       int   =						F"""{sampling_rate}"""
					_a      :       str   =						'''1'''
					_a      :       Optional[int]   =						'''f32le'''
					_a      :       Optional[Any]   =						[
					    '''ffmpeg''',
					    '''-i''',
					    '''pipe:0''',
					    '''-ac''',
					    ac,
					    '''-ar''',
					    ar,
					    '''-f''',
					    format_for_conversion,
					    '''-hide_banner''',
					    '''-loglevel''',
					    '''quiet''',
					    '''pipe:1''',
					]

					try:
										with subprocess.Popen(__a						,stdin=subprocess.PIPE						,stdout=subprocess.PIPE      ) as ffmpeg_process:
															_a      :       Any   =						ffmpeg_process.communicate(__a      )
					except FileNotFoundError as error:
										raise ValueError('''ffmpeg was not found but is required to load audio files from filename'''      ) from error
					_a      :       Optional[Any]   =						output_stream[0]
					_a      :       Optional[int]   =						np.frombuffer(__a						,np.floataa      )
					if audio.shape[0] == 0:
										raise ValueError('''Malformed soundfile'''      )
					return audio



def   __UpperCAmelCase					(      __a :     int						,__a :     float						,__a :     str = "f32le"						,)     ->	str:

					"""simple docstring"""

					_a      :       Dict   =						F"""{sampling_rate}"""
					_a      :       Optional[Any]   =						'''1'''
					if format_for_conversion == "s16le":
										_a      :       Dict   =						2
					elif format_for_conversion == "f32le":
										_a      :       Optional[Any]   =						4
					else:
										raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""      )

					_a      :       Dict   =						platform.system()
					if system == "Linux":
										_a      :       Dict   =						'''alsa'''
										_a      :       Union[str, Any]   =						'''default'''
					elif system == "Darwin":
										_a      :       Union[str, Any]   =						'''avfoundation'''
										_a      :       List[str]   =						''':0'''
					elif system == "Windows":
										_a      :       Optional[int]   =						'''dshow'''
										_a      :       str   =						'''default'''

					_a      :       Tuple   =						[
					    '''ffmpeg''',
					    '''-f''',
					    format_,
					    '''-i''',
					    input_,
					    '''-ac''',
					    ac,
					    '''-ar''',
					    ar,
					    '''-f''',
					    format_for_conversion,
					    '''-fflags''',
					    '''nobuffer''',
					    '''-hide_banner''',
					    '''-loglevel''',
					    '''quiet''',
					    '''pipe:1''',
					]
					_a      :       Any   =						int(round(sampling_rate * chunk_length_s      )      ) * size_of_sample
					_a      :       str   =						_ffmpeg_stream(__a						,__a      )
					for item in iterator:
										yield item



def   __UpperCAmelCase					(      __a :     int						,__a :     float						,__a :     Optional[int] = None						,__a :     Optional[Union[Tuple[float, float], float]] = None						,__a :     str = "f32le"						,)     ->	Optional[int]:

					"""simple docstring"""

					if stream_chunk_s is not None:
										_a      :       Tuple   =						stream_chunk_s
					else:
										_a      :       Tuple   =						chunk_length_s

					_a      :       Tuple   =						ffmpeg_microphone(__a						,__a						,format_for_conversion=__a      )
					if format_for_conversion == "s16le":
										_a      :       Any   =						np.intaa
										_a      :       Optional[int]   =						2
					elif format_for_conversion == "f32le":
										_a      :       Dict   =						np.floataa
										_a      :       List[Any]   =						4
					else:
										raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""      )

					if stride_length_s is None:
										_a      :       List[Any]   =						chunk_length_s / 6
					_a      :       Optional[int]   =						int(round(sampling_rate * chunk_length_s      )      ) * size_of_sample
					if isinstance(__a						,(int, float)      ):
										_a      :       Optional[Any]   =						[stride_length_s, stride_length_s]

					_a      :       Optional[Any]   =						int(round(sampling_rate * stride_length_s[0]      )      ) * size_of_sample
					_a      :       str   =						int(round(sampling_rate * stride_length_s[1]      )      ) * size_of_sample
					_a      :       Optional[Any]   =						datetime.datetime.now()
					_a      :       Tuple   =						datetime.timedelta(seconds=__a      )
					for item in chunk_bytes_iter(__a						,__a						,stride=(stride_left, stride_right)						,stream=__a      ):
										# Put everything back in numpy scale
										_a      :       Dict   =						np.frombuffer(item['''raw''']						,dtype=__a      )
										_a      :       Dict   =						(
										    item['''stride'''][0] // size_of_sample,
										    item['''stride'''][1] // size_of_sample,
										)
										_a      :       str   =						sampling_rate
										audio_time += delta
										if datetime.datetime.now() > audio_time + 10 * delta:
															# We're late !! SKIP
															continue
										yield item



def   __UpperCAmelCase					(      __a :     Optional[int]						,__a :     int						,__a :     Tuple[int, int]						,__a :     bool = False      )     ->	Optional[int]:

					"""simple docstring"""

					_a      :       Any   =						b''''''
					_a ,   _a      :       List[str]   =						stride
					if stride_left + stride_right >= chunk_len:
										raise ValueError(
										    F"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"""      )
					_a      :       List[str]   =						0
					for raw in iterator:
										acc += raw
										if stream and len(__a      ) < chunk_len:
															_a      :       Dict   =						(_stride_left, 0)
															yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
										else:
															while len(__a      ) >= chunk_len:
																				# We are flushing the accumulator
																				_a      :       List[str]   =						(_stride_left, stride_right)
																				_a      :       List[Any]   =						{'''raw''': acc[:chunk_len], '''stride''': stride}
																				if stream:
																									_a      :       List[Any]   =						False
																				yield item
																				_a      :       Optional[Any]   =						stride_left
																				_a      :       Optional[Any]   =						acc[chunk_len - stride_left - stride_right :]
    # Last chunk
					if len(__a      ) > stride_left:
										_a      :       Optional[Any]   =						{'''raw''': acc, '''stride''': (_stride_left, 0)}
										if stream:
															_a      :       Dict   =						False
										yield item







def   __UpperCAmelCase					(      __a :     int						,__a :     int      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Dict   =						2**24  # 16Mo
					try:
										with subprocess.Popen(__a						,stdout=subprocess.PIPE						,bufsize=__a      ) as ffmpeg_process:
															while True:
																				_a      :       int   =						ffmpeg_process.stdout.read(__a      )
																				if raw == b"":
																									break
																				yield raw
					except FileNotFoundError as error:
										raise ValueError('''ffmpeg was not found but is required to stream audio files from filename'''      ) from error










"""simple docstring"""





import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
       import onnxruntime as ort


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "tensor(bool)": np.bool_,
    "tensor(int8)": np.inta,
    "tensor(uint8)": np.uinta,
    "tensor(int16)": np.intaa,
    "tensor(uint16)": np.uintaa,
    "tensor(int32)": np.intaa,
    "tensor(uint32)": np.uintaa,
    "tensor(int64)": np.intaa,
    "tensor(uint64)": np.uintaa,
    "tensor(float16)": np.floataa,
    "tensor(float)": np.floataa,
    "tensor(double)": np.floataa,
}

class 						lowercase   :





      def __init__(			self ,		lowercase=None ,		**lowercase					)						->	int:
            logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""					)
            lowerCAmelCase    		=			model
            lowerCAmelCase    		=			kwargs.get("""model_save_dir""" ,		lowercase					)
            lowerCAmelCase    		=			kwargs.get("""latest_model_name""" ,		lowercase					)





      def __call__(			self ,		**lowercase					)						->	List[Any]:
            lowerCAmelCase    		=			{k: np.array(lowercase					) for k, v in kwargs.items()}
            return self.model.run(lowercase ,		lowercase					)





      @staticmethod
      def        _snake_case     (			lowercase ,		lowercase=None ,		lowercase=None					)						->	Optional[Any]:
            if provider is None:
                  logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""					)
                  lowerCAmelCase    		=			"""CPUExecutionProvider"""

            return ort.InferenceSession(lowercase ,		providers=[provider] ,		sess_options=lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase = None ,		**lowercase					)						->	str:
            lowerCAmelCase    		=			file_name if file_name is not None else ONNX_WEIGHTS_NAME

            lowerCAmelCase    		=			self.model_save_dir.joinpath(self.latest_model_name					)
            lowerCAmelCase    		=			Path(lowercase					).joinpath(lowercase					)
            try:
                  shutil.copyfile(lowercase ,		lowercase					)
            except shutil.SameFileError:
                  pass

            # copy external weights (for models >2GB)
            lowerCAmelCase    		=			self.model_save_dir.joinpath(lowercase					)
            if src_path.exists():
                  lowerCAmelCase    		=			Path(lowercase					).joinpath(lowercase					)
                  try:
                        shutil.copyfile(lowercase ,		lowercase					)
                  except shutil.SameFileError:
                        pass





      def        _snake_case     (			self ,		lowercase ,		**lowercase ,		)						->	str:
            if os.path.isfile(lowercase					):
                  logger.error(f'Provided path ({save_directory}) should be a directory, not a file'					)
                  return

            os.makedirs(lowercase ,		exist_ok=lowercase					)

            # saving model weights/files
            self._save_pretrained(lowercase ,		**lowercase					)





      @classmethod
      def        _snake_case     (			cls ,		lowercase ,		lowercase = None ,		lowercase = None ,		lowercase = False ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	Union[str, Any]:
            lowerCAmelCase    		=			file_name if file_name is not None else ONNX_WEIGHTS_NAME
            # load model from local directory
            if os.path.isdir(lowercase					):
                  lowerCAmelCase    		=			OnnxRuntimeModel.load_model(
                      os.path.join(lowercase ,		lowercase					) ,		provider=lowercase ,		sess_options=lowercase					)
                  lowerCAmelCase    		=			Path(lowercase					)
            # load model from hub
            else:
                  # download model
                  lowerCAmelCase    		=			hf_hub_download(
                      repo_id=lowercase ,		filename=lowercase ,		use_auth_token=lowercase ,		revision=lowercase ,		cache_dir=lowercase ,		force_download=lowercase ,		)
                  lowerCAmelCase    		=			Path(lowercase					).parent
                  lowerCAmelCase    		=			Path(lowercase					).name
                  lowerCAmelCase    		=			OnnxRuntimeModel.load_model(lowercase ,		provider=lowercase ,		sess_options=lowercase					)
            return cls(model=lowercase ,		**lowercase					)





      @classmethod
      def        _snake_case     (			cls ,		lowercase ,		lowercase = True ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	List[str]:
            lowerCAmelCase    		=			None
            if len(str(lowercase					).split("""@"""					)					) == 2:
                  lowerCAmelCase		,						lowerCAmelCase    		=			model_id.split("""@"""					)

            return cls._from_pretrained(
                model_id=lowercase ,		revision=lowercase ,		cache_dir=lowercase ,		force_download=lowercase ,		use_auth_token=lowercase ,		**lowercase ,		)






